


import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=13   ,    snake_case=7   ,    snake_case=True   ,    snake_case=True   ,    snake_case=True   ,    snake_case=True   ,    snake_case=99   ,    snake_case=32   ,    snake_case=5   ,    snake_case=4   ,    snake_case=37   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=16   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=4   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							seq_length
						UpperCamelCase__       =							is_training
						UpperCamelCase__       =							use_attention_mask
						UpperCamelCase__       =							use_token_type_ids
						UpperCamelCase__       =							use_labels
						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							type_sequence_label_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							num_choices




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size  )

						UpperCamelCase__       =							None
						if self.use_attention_mask:
									UpperCamelCase__       =							random_attention_mask([self.batch_size, self.seq_length]  )

						UpperCamelCase__       =							None
						if self.use_token_type_ids:
									UpperCamelCase__       =							ids_tensor([self.batch_size, self.seq_length]   ,    self.type_vocab_size  )

						UpperCamelCase__       =							BertConfig(
						    vocab_size=self.vocab_size   ,    hidden_size=self.hidden_size   ,    num_hidden_layers=self.num_hidden_layers   ,    num_attention_heads=self.num_attention_heads   ,    intermediate_size=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    hidden_dropout_prob=self.hidden_dropout_prob   ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    type_vocab_size=self.type_vocab_size   ,    is_decoder=snake_case   ,    initializer_range=self.initializer_range   ,    )

						return config, input_ids, token_type_ids, attention_mask




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.prepare_config_and_inputs()
						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							config_and_inputs
						UpperCamelCase__       =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.prepare_config_and_inputs()
						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							config_and_inputs

						UpperCamelCase__       =							True
						UpperCamelCase__       =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
						UpperCamelCase__       =							ids_tensor([self.batch_size, self.seq_length]   ,    vocab_size=2  )

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)







@require_flax
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Any 			=		True

			_UpperCamelCase		:       Tuple 			=		(
			    (
			        FlaxBertModel,
			        FlaxBertForPreTraining,
			        FlaxBertForMaskedLM,
			        FlaxBertForMultipleChoice,
			        FlaxBertForQuestionAnswering,
			        FlaxBertForNextSentencePrediction,
			        FlaxBertForSequenceClassification,
			        FlaxBertForTokenClassification,
			        FlaxBertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							FlaxBertModelTester(self  )







			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							FlaxBertModel.from_pretrained("bert-base-cased"  )
						UpperCamelCase__       =							model(np.ones((1, 1)  )  )
						self.assertIsNotNone(snake_case  )






def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


